ArticleNucleic acids research2018
Discovery of two-level modular organization from matched genomic data via joint matrix tri-factorization.
Article in Nucleic acids research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
17 citing papers in PubMed.
- Exploring Brain Imaging and Genetic Risk Factors in Different Progression States of Alzheimer's Disease Through OSnetNMF-Based Methods.Journal of molecular neuroscience : MN · 2025Article
- Dimension reduction, cell clustering, and cell-cell communication inference for single-cell transcriptomics with DcjComm.Genome biology · 2024Article
- HetFCM: functional co-module discovery by heterogeneous network co-clustering.Nucleic acids research · 2024Article
- Identification of the methotrexate resistance-related diagnostic markers in osteosarcoma via adaptive total variation netNMF and multi-omics datasets.Frontiers in genetics · 2023Article
- Associating brain imaging phenotypes and genetic risk factorsFrontiers in aging neuroscience · 2023Article
- Application of non-negative matrix factorization in oncology: one approach for establishing precision medicine.Briefings in bioinformatics · 2022Review
- MOMA: a multi-task attention learning algorithm for multi-omics data interpretation and classification.Bioinformatics (Oxford, England) · 2022Article
- PIntMF: Penalized Integrative Matrix Factorization method for multi-omics data.Bioinformatics (Oxford, England) · 2022Article
- Predicting deleterious missense genetic variants via integrative supervised nonnegative matrix tri-factorization.Scientific reports · 2021Article
- CBP-JMF: An Improved Joint Matrix Tri-Factorization Method for Characterizing Complex Biological Processes of Diseases.Frontiers in genetics · 2021Article
- NMFNA: A Non-negative Matrix Factorization Network Analysis Method for Identifying Modules and Characteristic Genes of Pancreatic Cancer.Frontiers in genetics · 2021Article
- Interpretable, Scalable, and Transferrable Functional Projection of Large-Scale Transcriptome Data Using Constrained Matrix Decomposition.Frontiers in genetics · 2021Article
- MHSNMF: multi-view hessian regularization based symmetric nonnegative matrix factorization for microbiome data analysis.BMC bioinformatics · 2020Article
- Integrative Analysis for Identifying Co-Modules of Microbe-Disease Data by Matrix Tri-Factorization With Phylogenetic Information.Frontiers in genetics · 2020Article
- A network clustering based feature selection strategy for classifying autism spectrum disorder.BMC medical genomics · 2019Article
- Learning common and specific patterns from data of multiple interrelated biological scenarios with matrix factorization.Nucleic acids research · 2019Article
- Network-guided prediction of aromatase inhibitor response in breast cancer.PLoS computational biology · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With the rapid development of biotechnology, multi-dimensional genomic data are available for us to study the regulatory associations among multiple levels. Thus, it is essential to develop a tool to identify not only the modular patterns from multiple levels, but also the relationships among these modules. In this study, we adopt a novel non-negative matrix factorization framework (NetNMF) to integrate pairwise genomic data in a network manner. NetNMF could reveal the modules of each dimension and the connections within and between both types of modules. We first demonstrated the effectiveness of NetNMF using a set of simulated data and compared it with two typical NMF methods. Further, we applied it to two different types of pairwise genomic datasets including microRNA (miRNA) and gene expression data from The Cancer Genome Atlas and gene expression and pharmacological data from the Cancer Genome Project. We respectively identified a two-level miRNA-gene module network and a two-level gene-drug module network. Not only have the majority of identified modules significantly functional implications, but also the three types of module pairs have closely biological associations. This module discovery tool provides us comprehensive insights into the mechanisms of how the two levels of molecules cooperate with each other.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.